编码关键信息：面向自动驾驶节能感知的语义感知型内存接口

中文摘要 AI 辅助

自动驾驶车辆在感知运行前会将高分辨率环绕摄像头帧传输至内存。该传感器到内存的路径在存储1的单元和数据总线上相邻字节翻转时会消耗能量，因此其成本取决于1比特密度和翻转活动，而非像素语义。本文提出MotiMem-Omega，一种语义感知型内存接口编码器，可在保留感知预测的同时降低该成本。其语义重要性字段会保护交通参与者，尤其是弱势道路使用者，同时对天空和空旷背景分配较低保真度。跨数据集比特敏感性扫描确定类别权重，为行人、骑行者和摩托车手设置安全下限。随后每个图像块通过最小化联合能量-失真成本选择精度层级。当自车姿态可用时，运动补偿先验会在帧间传递受保护区域。我们使用系数扫描的内存能量模型，从两个测量代理估计接口能量降低量。在12个驾驶数据集上的29种检测器、5种占用模型和5种分割网络中，MotiMem-Omega保留了约90%的检测平均精度、91%的弱势道路使用者召回率、超过98%的占用精度，且在节能方法中实现最强的分割保留。它将前置摄像头的1比特密度降低52%，对应建模的内存接口能量降低近36%，在文献系数扫描下下限为27%。在相同或更低的1比特密度下，其精度保留优于基线和能量匹配截断，而图像编解码器在不降低内存接口能量的情况下保留精度。

英文摘要

Autonomous vehicles stream high-resolution surround-camera frames into memory before perception runs. This sensor-to-memory path consumes energy when cells store ones and adjacent bytes toggle on the data bus, so its cost follows bit-1 density and switching activity rather than pixel semantics. We present MotiMem-Omega, a semantic-aware memory-interface coder that lowers this cost while preserving perception predictions. Its semantic importance field protects traffic participants, especially vulnerable road users, while assigning lower fidelity to sky and empty background. Cross-dataset bit-sensitivity sweeps determine class weights, with a safety floor for pedestrians, cyclists, and motorcyclists. Each image block then selects a precision tier by minimizing a joint energy-distortion cost. When ego pose is available, a motion-compensated prior carries protected regions between frames. We estimate interface-energy reduction from the two measured proxies using a coefficient-swept memory-energy model. Across 29 detectors on 12 driving datasets, 5 occupancy models, and 5 segmentation networks, MotiMem-Omega retains about 90% of detection mean average precision, 91% of vulnerable-road-user recall, over 98% of occupancy accuracy, and the strongest segmentation retention among energy-reducing methods. It reduces front-camera bit-1 density by 52%, corresponding to a modeled memory-interface energy reduction near 36%, with a lower end of 27% under the literature coefficient sweep. It also gives higher retention than the baseline and energy-matched truncation at the same or lower bit-1 density, whereas image codecs preserve accuracy without reducing memory-interface energy.

发表机构

• College of Engineering, University of Georgia（佐治亚大学工程学院）

机构由 AI 辅助整理，请以论文原文为准。